关键词： Semiconductor manufacturing   wafer defects   pattern recognition   generative adversarial networks  

摘要： Identification of the defective patterns of the wafer maps can provide insights for the quality control in the semiconductor wafer fabrication systems (SWFSs). In real SWFSs, the collected wafer maps are usually imbalanced from the defective types, which will result in misidentification. In this paper, a novel deep learning model called adaptive balancing generative adversarial network (AdaBalGAN) is proposed for the defective pattern recognition (DPR) of wafer maps with imbalanced data. In addition, a categorical generative adversarial network is improved to generate simulated wafer maps in high fidelity and classify the patterns with high accuracy for all defective categories. Taking consideration of the various learning abilities of the DPR model for different patterns into account, an adaptive generative controller is designed to balance the number of samples of each defective type according to the classification accuracy. The experiment results indicated that the proposed AdaBalGAN model outperforms conventional models with higher accuracy and stability for the DPR of wafer maps. Further results of comparative experiments revealed that the proposed adaptive generative mechanism can enhance and balance the recognition accuracy for all categories in the DPR of wafer maps.

关键词： Intelligent ensemble classifier   synthetic minority over-sampling technique   water-flooded layer identification   curve fitting   oil and gas recognition system  

摘要： Since the actual chromatogram data of the water-flooded layer have characteristics of multiple dimension, complexity and noise, it is difficult to accurately identify and appraise the water-flooded layer in the oil and gas reservoirs. Therefore, this article proposes a recognition modeling approach based on the intelligent ensemble classifier, integrated model-free Bayesian classifier, the AdaBoost algorithm and the support vector machine algorithm. The effective chromatogram characteristic information can be obtained using the curve fitting method. In order to transform the sparse classification problem into a general classification problem, the synthetic minority over-sampling technique algorithm is used to process an unbalanced training sample as a general training sample. Moreover, the model-free Bayesian classifier, AdaBoost and support vector machine algorithms are used as the base classifiers to train the ensemble classification model. Compared to the traditional single classification approach, the robustness and the effectiveness of the ensemble classifier model are validated through the standard data source from the UCI (University of California at Irvine) repository. Finally, the proposed model is applied in the identification and appraisal of the water-flooded layers in a complex oil and gas recognition system. The chromatogram characteristic information and the prediction results are obtained to provide more reliable water-flooded layer information, guide the process of reservoir exploration and improve the oil development efficiency.

关键词： Activity-based model   Time-use   Machine learning   Fuzzy C-means clustering algorithm   Activity pattern recognition  

摘要： This study develops a new comprehensive pattern recognition modeling framework that leverages activity data to derive clusters of homogeneous daily activity patterns, for use in activity-based travel demand modeling. The pattern recognition model is applied to time use data from the large Halifax STAR household travel diary survey. Several machine learning techniques not previously employed in travel behavior analysis are used within the pattern recognition modeling framework. Pattern complexity of activity sequences in the dataset was recognized using the FCM algorithm, and resulted in identification of twelve unique clusters of homogeneous daily activity patterns. We then analysed inter-dependencies in each identified cluster and characterized the cluster memberships through their socio-demographic attributes using the CART classifier. Based on the socio-demographic characteristics of individuals we were able to correctly identify which cluster individuals belonged to, and also predict various information related to their activities, such as start time, duration, travel distance, and travel mode, for use in activitybased travel demand modeling. To execute the pattern recognition model, the 24-h activity patterns are split into 288 three dimensional 5 min intervals. Each interval includes information on activity types, duration, start time, location, and travel mode if applicable. Results from aggregated statistical evaluation and Kolmogorov-Smirnov tests indicate that there is heterogeneous diversity among identified clusters in terms of temporal distribution, and substantial differences in a variety of socio-demographic variables. The homogeneous clusters identified in this study may be used to more accurately predict the scheduling behavior of specific population groups in activity-based modeling, and hence to improve prediction of the times and locations of their travel demands. Finally, the results of this study are expected to be implemented within the activi

摘要： Breast cancer (BC), ranked as the fifth amongst all cancers, remains at the top of women's cancers worldwide followed by colorectal, lung, cervix, and stomach cancers. The main handicap of most of the screening/diagnostic methods is based on their low sensitivity and specificity and the invasive behavior of most sampling procedures. The aim of this study was to establish the volatomic pattern of BC and cancer-free (CF) tissues (n = 30) from the same patients, as a powerful tool to identify a set of volatile organic metabolite (VOM) potential BC biomarkers which might be used together or complement with the traditional BC diagnostics strategies, through the integration of chromatographic data, obtained by solid-phase microextraction followed by gas chromatography-mass spectrometry (SPME/GC-qMS), with chemometric tools. A total of four metabolites: limonene, decanoic acid, acetic acid and furfural presented the highest contribution towards discrimination of BC and CF tissues (VIP > 1, p < 0.05). The discrimination efficiency and accuracy of BC tissue metabolites was ascertained by ROC curve analysis that allowed the identification of some metabolites with high sensitivity and specificity. The results obtained with this approach suggest the possibility of identifying endogenous metabolites as a platform to find potential BC biomarkers and pave the way to investigate the related metabolomic pathways in order to improve BC diagnostic tools. Moreover, deeper investigations could unravel novel mechanistic insights into the disease pathophysiology.

关键词： Evodia rutaecarpa   chemometrics   source   characteristics  

摘要： In this study, the appearance and texture of E. rutaecarpa were linked with the chemical constituents to explore methods of classification of E. rutaecarpa. The Chemometrics such as Hierarchical cluster analysis (HCA), principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models were used for analysis. According to the models, samples of E. rutaecarpa were divided into three categories based on their source: Evodia, Stone Tiger and Sparse Evodia. The Evodia category could be subdivided into two categories, one representing large fruits with a greater degree of cracking and the other representing large fruits with little or no cracking. The method provided by this study combines chemometrics with HPLC fingerprints, which can provide a basis and reference for the identification of E. rutaecarpa and enables establishment of a grade standard. [GRAPHICS] .

关键词： Corrosion   corrosion mechanism   electrochemical noise   pattern recognition  

摘要： Uniform corrosion, pitting corrosion and passivation of 304 stainless steel were promoted and pattern recognition system (PRS) based on electrochemical noise (EN) measurements was employed to classify these corrosion mechanisms in laboratory-scale. The EN signals were analyzed using statistical, shot noise, wavelet and recurrence quantification analysis. It was found that the PRS designed by combining these EN parameters has a discriminant accuracy of 99.7%, which is superior to those designed by solely statistical parameters (92.4%), and the parameters selected by principle component analysis (97.8%). The application of the PRS above is evaluated by considering environmental factors and material types. (C) 2019 The Electrochemical Society.

关键词： 模式识别技术   生物医学工程   应用  

摘要： 模式识别技术是当前最为常用的一种人工智能技术,当前模式识别技术在生物医学工程中得到了非常广泛的应用,并且有效促进了生物医学工程学科教育的进一步发展。本文对模式识别技术进行了简单的介绍,之后具体分析了模式识别技术在生物医学工程专业中的应用情况,希望有助于进一步推动模式识别技术在生物医学工程专业教育领域的应用,并以此来促进生物医学工程专业领域的发展。

关键词： 模式识别   课程改革   深度学习   人工智呢   实验平台  

摘要： 随着这人工智能算法尤其是深度学习的快速发展,已经对各行各业产生了影响。为了能够跟上技术和理论发展的步伐,培养企业和行业所需的人才,当前高校的人工智能课程需要做出调整。本文以模式识别课程为例,探讨人工智能背景下高校课程的改革方案。首先分析了目前模式识别课程存在的问题。然后,针对模式识别的课程特点,提出将前沿科技引入教学内容,并对教学内容和教学方式进行改革。最后,开发了相应的实验平台,并在实验教学环节中介绍了基于深度卷积神经网络手写体识别的实验案例。

关键词： Wastewater treatment plant (WWTP)   Influent data   K-means clustering   Climatic data   Python (TM)  

摘要： This study proposes an integrated approach by combining a pattern recognition technique and a process simulation model, to assess the impact of various climatic conditions on influent characteristics of the largest Italian wastewater treatment plant (WWTP) at Castiglione Torinese. Eight years (viz. 2009-2016) of historical influent data namely influent flow rate (Q(in)), chemical oxygen demand (COD), ammonium (N-NH4) and total suspended solids (TSS), in addition to two climatic attributes, average temperature and daily mean precipitation rates (P-I) from the plant catchment area, are evaluated in this study. Following the outlier removal and missing data imputation, five influent climate-based scenarios are identified by K-means clustering approach. Statistical characteristics of clustered observations are further investigated. Finally, to demonstrate that the proposed approach could improve the process control and efficiency, a process simulation model was developed and calibrated. Steady-state simulations were conducted, and the performance of the plant was studied under five influent scenarios. Further, an optimization scenario-based method was conducted to improve the energy consumption of the plant while meeting effluent requirements. The results indicate that with the adaptation of suitable aeration strategies for each of the influent scenarios, 10-40% energy saving can be achieved while meeting effluent requirements.

关键词： Volatile organic compounds  

摘要： The volatile organic compounds produced in yeast fermentation are directly related to the degree of fermentation and product quality. This study innovatively proposes a method based on an olfactory visualization sensor system combined with a pattern recognition algorithm to ensure the correct discrimination of the yeast fermentation stages. First, the olfactory visualization sensor system was developed based on a colorimetric sensor array, which was composed of twelve chemical dyes including eleven porphyrins or metalloporphyrins and one pH indicator on a C2 reverse silica-gel flat plate. It was employed as an artificial olfactory sensor system to obtain odor information during the process of yeast fermentation. Then, principal component analysis (PCA) was used to reduce the dimension of the data, which were obtained from the olfactory visualization sensor system. Finally, three pattern recognition algorithms, i.e., support vector machine (SVM), extreme learning machine (ELM) and random forest (RF), were used to develop identification models for monitoring the yeast fermentation stages. The results showed that the optimum SVM model was superior to the ELM and RF models with a discrimination rate of 100% in the prediction process. The overall results sufficiently demonstrate that the olfactory visualization sensor system integrated with an appropriate pattern recognition algorithm has a promising potential for the in situ monitoring of yeast fermentation.